The Experts below are selected from a list of 1845 Experts worldwide ranked by ideXlab platform
Michel Verhaegen - One of the best experts on this subject based on the ideXlab platform.
-
Robust Air Data Sensor Fault Diagnosis With Enhanced Fault Sensitivity Using Moving Horizon Estimation
arXiv: Systems and Control, 2016Co-Authors: Yiming Wan, Tamas Keviczky, Michel VerhaegenAbstract:This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using Moving Horizon Estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated Moving Horizon Estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the Moving Horizon Estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply decrease as one would expect in conventional unconstrained residual generators. Instead, its fault sensitivity increases when the fault is large enough to activate some inequality constraints. This fault sensitivity improvement is not restricted to this particular application, but can be achieved by any general Moving Horizon Estimation based residual generator. A high-fidelity Airbus simulator is used to illustrate the advantage of our proposed approach in terms of fault sensitivity.
-
ACC - Robust air data sensor fault diagnosis with enhanced fault sensitivity using Moving Horizon Estimation
2016 American Control Conference (ACC), 2016Co-Authors: Yiming Wan, Tamas Keviczky, Michel VerhaegenAbstract:This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using Moving Horizon Estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated Moving Horizon Estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the Moving Horizon Estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply decrease as one would expect in conventional unconstrained residual generators. Instead, its fault sensitivity increases when the fault is large enough to activate some inequality constraints. This fault sensitivity improvement is not restricted to this particular application, but can be achieved by any general Moving Horizon Estimation based residual generator. A high-fidelity Airbus simulator is used to illustrate the advantage of our proposed approach in terms of fault sensitivity.
-
Moving Horizon Estimation for Large-Scale Interconnected Systems
IEEE Transactions on Automatic Control, 2013Co-Authors: Aleksandar Haber, Michel VerhaegenAbstract:We present computationally efficient centralized and distributed Moving Horizon Estimation (MHE) methods for large-scale interconnected systems, that are described by sparse banded or sparse multibanded system matrices. Both of these MHE methods are developed by approximating a solution of the MHE problem using the Chebyshev approximation method. By exploiting the sparsity of this approximate solution we derive a centralized MHE method, which computational complexity and storage requirements scale linearly with the number of local subsystems of an interconnected system. Furthermore, on the basis of the approximate solution of the MHE problem, we develop a novel, distributed MHE method. This distributed MHE method estimates the state of a local subsystem using only local input-output data. In contrast to the existing distributed algorithms for the state Estimation of large-scale systems, the proposed distributed MHE method is not relying on the consensus algorithms and has a simple analytic form. We have studied the stability of the proposed MHE methods and we have performed numerical simulations that confirm our theoretical results.
Yiming Wan - One of the best experts on this subject based on the ideXlab platform.
-
Implementation of real-time Moving Horizon Estimation for robust air data sensor fault diagnosis in the RECONFIGURE benchmark
arXiv: Systems and Control, 2016Co-Authors: Yiming Wan, Tamas KeviczkyAbstract:This paper presents robust fault diagnosis and Estimation for the calibrated airspeed and angle-of-attack sensor faults in the RECONFIGURE benchmark. We adopt a low-order longitudinal model augmented with wind dynamics. In order to enhance sensitivity to faults in the presence of winds, we propose a constrained residual generator by formulating a constrained Moving Horizon Estimation problem and exploiting the bounds of winds. The Moving Horizon Estimation problem requires solving a nonlinear program in real time, which is challenging for flight control computers. This challenge is addressed by adopting an efficient structure-exploiting algorithm within a real-time iteration scheme. Specific approximations and simplifications are performed to enable the implementation of the algorithm using the Airbus graphical symbol library for industrial validation and verification. The simulation tests on the RECONFIGURE benchmark over different flight points and maneuvers show the efficacy of the proposed approach.
-
Robust Air Data Sensor Fault Diagnosis With Enhanced Fault Sensitivity Using Moving Horizon Estimation
arXiv: Systems and Control, 2016Co-Authors: Yiming Wan, Tamas Keviczky, Michel VerhaegenAbstract:This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using Moving Horizon Estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated Moving Horizon Estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the Moving Horizon Estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply decrease as one would expect in conventional unconstrained residual generators. Instead, its fault sensitivity increases when the fault is large enough to activate some inequality constraints. This fault sensitivity improvement is not restricted to this particular application, but can be achieved by any general Moving Horizon Estimation based residual generator. A high-fidelity Airbus simulator is used to illustrate the advantage of our proposed approach in terms of fault sensitivity.
-
ACC - Robust air data sensor fault diagnosis with enhanced fault sensitivity using Moving Horizon Estimation
2016 American Control Conference (ACC), 2016Co-Authors: Yiming Wan, Tamas Keviczky, Michel VerhaegenAbstract:This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using Moving Horizon Estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated Moving Horizon Estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the Moving Horizon Estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply decrease as one would expect in conventional unconstrained residual generators. Instead, its fault sensitivity increases when the fault is large enough to activate some inequality constraints. This fault sensitivity improvement is not restricted to this particular application, but can be achieved by any general Moving Horizon Estimation based residual generator. A high-fidelity Airbus simulator is used to illustrate the advantage of our proposed approach in terms of fault sensitivity.
Tamas Keviczky - One of the best experts on this subject based on the ideXlab platform.
-
Implementation of real-time Moving Horizon Estimation for robust air data sensor fault diagnosis in the RECONFIGURE benchmark
arXiv: Systems and Control, 2016Co-Authors: Yiming Wan, Tamas KeviczkyAbstract:This paper presents robust fault diagnosis and Estimation for the calibrated airspeed and angle-of-attack sensor faults in the RECONFIGURE benchmark. We adopt a low-order longitudinal model augmented with wind dynamics. In order to enhance sensitivity to faults in the presence of winds, we propose a constrained residual generator by formulating a constrained Moving Horizon Estimation problem and exploiting the bounds of winds. The Moving Horizon Estimation problem requires solving a nonlinear program in real time, which is challenging for flight control computers. This challenge is addressed by adopting an efficient structure-exploiting algorithm within a real-time iteration scheme. Specific approximations and simplifications are performed to enable the implementation of the algorithm using the Airbus graphical symbol library for industrial validation and verification. The simulation tests on the RECONFIGURE benchmark over different flight points and maneuvers show the efficacy of the proposed approach.
-
Robust Air Data Sensor Fault Diagnosis With Enhanced Fault Sensitivity Using Moving Horizon Estimation
arXiv: Systems and Control, 2016Co-Authors: Yiming Wan, Tamas Keviczky, Michel VerhaegenAbstract:This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using Moving Horizon Estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated Moving Horizon Estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the Moving Horizon Estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply decrease as one would expect in conventional unconstrained residual generators. Instead, its fault sensitivity increases when the fault is large enough to activate some inequality constraints. This fault sensitivity improvement is not restricted to this particular application, but can be achieved by any general Moving Horizon Estimation based residual generator. A high-fidelity Airbus simulator is used to illustrate the advantage of our proposed approach in terms of fault sensitivity.
-
ACC - Robust air data sensor fault diagnosis with enhanced fault sensitivity using Moving Horizon Estimation
2016 American Control Conference (ACC), 2016Co-Authors: Yiming Wan, Tamas Keviczky, Michel VerhaegenAbstract:This paper investigates robust fault diagnosis of multiple air data sensor faults in the presence of winds. The trade-off between robustness to winds and sensitivity to faults is challenging due to simultaneous influence of winds and latent faults on monitored sensors. Different from conventional residual generators that do not consider any constraints, we propose a constrained residual generator using Moving Horizon Estimation. The main contribution is improved fault sensitivity by exploiting known bounds on winds in residual generation. By analyzing the Karush-Kuhn-Tucker conditions of the formulated Moving Horizon Estimation problem, it is shown that this improvement is attributed to active inequality constraints caused by faults. When the weighting matrices in the Moving Horizon Estimation problem are tuned to increase robustness to winds, its fault sensitivity does not simply decrease as one would expect in conventional unconstrained residual generators. Instead, its fault sensitivity increases when the fault is large enough to activate some inequality constraints. This fault sensitivity improvement is not restricted to this particular application, but can be achieved by any general Moving Horizon Estimation based residual generator. A high-fidelity Airbus simulator is used to illustrate the advantage of our proposed approach in terms of fault sensitivity.
Matthias A Muller - One of the best experts on this subject based on the ideXlab platform.
-
robust global exponential stability for Moving Horizon Estimation
Conference on Decision and Control, 2018Co-Authors: Sven Knufer, Matthias A MullerAbstract:In this paper, we consider optimization-based state Estimation for general detectable nonlinear systems subject to unknown disturbances. The main contribution is a novel formulation of the cost function and a novel proof technique, which allows us (i) to ensure robust global exponential stability of the Estimation error under a suitable exponential detectability condition and (ii) to overcome several of the drawbacks in the existing literature. In particular, we obtain improved estimates for the disturbance gains and the required minimal Estimation Horizon (which are independent of some maximum a priori disturbance bound), and provide a unified proof technique which can be used for both full information Estimation and Moving Horizon Estimation.
-
CDC - Robust Global Exponential Stability for Moving Horizon Estimation
2018 IEEE Conference on Decision and Control (CDC), 2018Co-Authors: Sven Knufer, Matthias A MullerAbstract:In this paper, we consider optimization-based state Estimation for general detectable nonlinear systems subject to unknown disturbances. The main contribution is a novel formulation of the cost function and a novel proof technique, which allows us (i) to ensure robust global exponential stability of the Estimation error under a suitable exponential detectability condition and (ii) to overcome several of the drawbacks in the existing literature. In particular, we obtain improved estimates for the disturbance gains and the required minimal Estimation Horizon (which are independent of some maximum a priori disturbance bound), and provide a unified proof technique which can be used for both full information Estimation and Moving Horizon Estimation.
Chen Hong - One of the best experts on this subject based on the ideXlab platform.
-
CDC - Robust Moving Horizon Estimation for system with uncertain measurement output
Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009Co-Authors: Zhao Haiyan, Chen Hong, Ma YanAbstract:A Moving Horizon Estimation (MHE) method of the constrained system with uncertain measurement output and norm-bounded parameter uncertainties is proposed. Based on the probability of known error of the output signal, the initial state variables and disturbance are estimated by minimizing performance object at each sampling moment. The Estimation values of the state variables are calculated by the optimization theory of Model Predictive Control (MPC). Simulation results show that MHE method has better ability than the method of the robust filter.
-
Moving Horizon Estimation approach to constrained systems with uncertain noise covariance
Control and Decision, 2008Co-Authors: Chen HongAbstract:For the constraint system of covariance matrix being uncertain,a robust Moving Horizon Estimation(MHE) strategy is discussed.The upper bound of minimum error covariance satisfying all the uncertainties is found and the optimal problem is solved in the framework of linear matrix inequality(LMI).Based on the Moving Horizon strategy,the state is estimated by minimizing performance object while satisfying the constraints for every possible noise covariance within the given bounds.Simulation and comparison results show with the robust Kalman filter are given.The results the effectiveness of the method.
-
Moving Horizon Estimation for stochastic systems with unknown inputs
Electric Machines and Control, 2007Co-Authors: Chen HongAbstract:A Moving Horizon Estimation(MHE) strategy is discussed on the system with the uncertain disturbance inputs.An estimator unaffected from the unknown disturbance inputs is obtained with the properties of unbiased minimum variance.Based on the Moving Horizon strategy,the state can be estimated by minimizing the performance object of the optimal problem in every sampling time.Simulation results and comparison results with the recursive filter are given,and the results indicate that the method of MHE is more effective than the constrained linear system.
-
Application of Moving Horizon Estimation in three tank system
Journal of Changchun Post and Telecommunication Institute, 2004Co-Authors: Zhao Hai-yan, Chen HongAbstract:A MHE(Moving Horizon Estimation) approach that can make use of additional knowledge to achieve improvements in the Estimation performance is presented. Some practical and theoretical properties of MHE are discussed. Simulation results for a three tank system and comparisons with the Kalman estimator are given,which indicate the more effectiveness of MHE than Kalman filter for the constrained linear system.
